is hence strongly optimal in both the optimal prediction and the prescription problems.
In the exponential regime, \(a_T\sim rT\) , \(r>0\) , we extend results in [1]} to our more general setting and show that the DRO formulation (REF ) with KL ambiguity set is strongly optimal in both the optimal prediction and prescription problems even when allowed to explicitly depend of the data size. This implies that consistency is impossible in both the exponential and superexponential regime, and imposing such out-of-sample guarantees yields rather conservative predictors.
